There are three commonly used GBDT variations in the literature: extreme gradient boosting (XGBoost) [1]}, light gradient boosting machine (LightGBM) [2]}, and categorical boosting (CatBoost) [3]}. As an example of XGBoost, Pang et al. [4]} propose a subcellular localisation method by integrating the Convolutional Neural Network (CNN) and XGBoost, where CNN acts as a feature extractor and XGBoost acts as a classifier to identify the protein subcellular localisation. In another literature review, Torres-Barrán et al. [5]} study the application of XGBoost to global and local wind energy prediction and solar radiation problem, exploiting gradient boosting regression methods. As for LightGBM, Ju et al. in [6]} overcome the limitation of the single-convolution model in predicting the wind power by integrating the LightGBM
algorithm to improve the robustness and accuracy of the forecasting.
